Segmentation of Images by using Fuzzy k-means clustering with ACO
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
Index Terms— ACO, Fitness Function, Fuzzy, K-means
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL